Progenitor effects and unique transcriptomic signatures linked to differentiation phenotype in clonally expanded antigen-specific CD8 T memory stem cells (TSCM)
Single-cell RNA-seq · human · 509 samples
- What it is
- Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
- Organism
- Homo sapiens (human)
- Samples
- 509 samples: 114 conditions, mostly in duplicateGroups are read from the sample names, not from the paper. Check the list below.
- Data you can get
- A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
- Paper
- Progenitor effects and unique transcriptomic signatures linked to differentiation phenotype in clonally expanded antigen-specific CD8 T memory stem cellsJournal of immunology (Baltimore, Md. : 1950) · 2026
Read the full summary ↓
Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.
Processed table
The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.
Supplementary files
The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE280113_RAW.tar (it can run to tens of gigabytes).
Raw sequencing reads
The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.
What the files usually look like: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.
Also filed as BioProject PRJNA1176481 and SRA study SRP540875. Searching any of these in the dataset finder brings you back here.
Supports a between-group comparison across 307 samples.
How we read this: 114 replicated groups read from the first 400 of 509 sample titles; they account for 307 of them. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.
- GSM8587565 697-CM-CL1-CM-B06
- GSM8587566 697-CM-CL1-CM-C02 697-CM-CL1-CM-C
- GSM8587567 697-CM-CL1-CM-C08 697-CM-CL1-CM-C
- GSM8587568 697-CM-CL1-CM-D04 697-CM-CL1-CM-D
- GSM8587569 697-CM-CL1-CM-D06 697-CM-CL1-CM-D
- GSM8587570 697-CM-CL1-CM-E12
- GSM8587571 697-CM-CL1-CM-F05
- GSM8587572 697-CM-CL1-CM-G07
- GSM8587573 697-CM-CL1-CM-H03 697-CM-CL1-CM-H
- GSM8587574 697-CM-CL1-CM-H06 697-CM-CL1-CM-H
- GSM8587575 697-CM-CL1-EFF-B05
- GSM8587576 697-CM-CL1-EFF-C03 697-CM-CL1-EFF-C
- GSM8587577 697-CM-CL1-EFF-C05 697-CM-CL1-EFF-C
- GSM8587578 697-CM-CL1-EFF-C06 697-CM-CL1-EFF-C
- GSM8587579 697-CM-CL1-EFF-E03 697-CM-CL1-EFF-E
- GSM8587580 697-CM-CL1-EFF-E10 697-CM-CL1-EFF-E
- GSM8587581 697-CM-CL1-EFF-F04 697-CM-CL1-EFF-F
- GSM8587582 697-CM-CL1-EFF-F06 697-CM-CL1-EFF-F
- GSM8587583 697-CM-CL1-EFF-F07 697-CM-CL1-EFF-F
- GSM8587584 697-CM-CL1-EFF-H02
- GSM8587585 697-CM-CL1-EM-B07
- GSM8587586 697-CM-CL1-EM-D10
- GSM8587587 697-CM-CL1-EM-E05
- GSM8587588 697-CM-CL1-EM-F10 697-CM-CL1-EM-F
- GSM8587589 697-CM-CL1-EM-F11 697-CM-CL1-EM-F
- GSM8587590 697-CM-CL1-EM-G06 697-CM-CL1-EM-G
- GSM8587591 697-CM-CL1-EM-G08 697-CM-CL1-EM-G
- GSM8587592 697-CM-CL1-EM-G12 697-CM-CL1-EM-G
- GSM8587593 697-CM-CL1-EM-H09
- GSM8587594 697-CM-CL1-SCM-A03
- GSM8587595 697-CM-CL1-SCM-B04 697-CM-CL1-SCM-B
- GSM8587596 697-CM-CL1-SCM-B09 697-CM-CL1-SCM-B
- GSM8587597 697-CM-CL1-SCM-B12 697-CM-CL1-SCM-B
- GSM8587598 697-CM-CL1-SCM-C04 697-CM-CL1-SCM-C
- GSM8587599 697-CM-CL1-SCM-C09 697-CM-CL1-SCM-C
- GSM8587600 697-CM-CL1-SCM-E04 697-CM-CL1-SCM-E
- GSM8587601 697-CM-CL1-SCM-E09 697-CM-CL1-SCM-E
- GSM8587602 697-CM-CL1-SCM-F09
- GSM8587603 697-CM-CL1-SCM-G03 697-CM-CL1-SCM-G
- GSM8587604 697-CM-CL1-SCM-G09 697-CM-CL1-SCM-G
+ 469 more — browse all 509 samples with per-sample file links →
- GSE279781 CD137 agonism enhances anti-PD1 induced activation of clonally expanded CD8+ T cells in a neoadjuvant pancreatic cancer clinical trial 30 samples shares cd8, clonally, expanded
- GSE272993 Combination anti-PD-1 and anti-CTLA-4 therapy generates waves of clonal responses that include progenitor-exhausted CD8 T cells [dataset 1] 360 samples shares cd8, progenitor
- GSE266862 Single-cell RNA sequencing comparison of CD4+, CD8+ and T-cell receptor γδ+ cutaneous T-cell lymphomas reveals subset-specific molecular phenotypes 44 samples shares cd8, phenotype
- GSE275634 ATAC-seq of antigen-specific memory CD8+ T cells 41 samples shares cd8, antigen-specific
- GSE242232 Autoantigen-specific CD4+ T cells acquire an exhausted phenotype and persist in human antigen-specific autoimmune diseases 928 samples shares antigen-specific, phenotype
- GSE235093 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy 333 samples shares cd8
- GSE235090 Pre-existing skin-resident CD8 and γδ T cell circuits mediate immune response in Merkel cell carcinoma and Predict immunotherapy efficacy [scRNA_tissue] 192 samples shares cd8
- GSE295725 Multimodal delineation of a layer of effector function among exhausted CD8 T cells in tumors 98 samples shares cd8
Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.